Uncertainty estimation in multi-LLM systems remains largely single-model-centric: existing methods quantify uncertainty within each model but do not adequately capture semantic disagreement across models. To address this gap, we propose Collaborative Entropy (CoE), a unified information-theoretic metric for semantic uncertainty in multi-LLM collaboration. CoE is defined on a shared semantic cluster space and combines two components: intra-model semantic entropy and inter-model divergence to the ensemble mean. CoE is not a weighted ensemble predictor; it is a system-level uncertainty measure that characterizes collaborative confidence and disagreement. We analyze several core properties of CoE, including non-negativity, zero-value certainty under perfect semantic consensus, and the behavior of CoE when individual models collapse to delta distributions. These results clarify when reducing per-model uncertainty is sufficient and when residual inter-model disagreement remains. We also present a simple CoE-guided, training-free post-hoc coordination heuristic as a practical application of the metric. Experiments on \textit{TriviaQA} and \textit{SQuAD} with LLaMA-3.1-8B-Instruct, Qwen-2.5-7B-Instruct, and Mistral-7B-Instruct show that CoE provides stronger uncertainty estimation than standard entropy- and divergence-based baselines, with gains becoming larger as additional heterogeneous models are introduced. Overall, CoE offers a useful uncertainty-aware perspective on multi-LLM collaboration.
Agentic AI-based Framework for Mitigating Premature Diagnostic Handoff and Silent Hallucination in Healthcare Applications
Distributed Systems (qCLOUDS) Lab, School of Computing；Information Systems, The University of Melbourne, Australia；2Department of Computer Science；Engineering, School of Electrical；Computer Sciences (SECS), Indian Institute of Technology Bhubaneswar, India；3Department of Computer Science Banaras Hindu University, Varanasi, India
Agentic-J: An AI Agent for Biological Microscopy Image Analysis
University of Tartu（塔尔图大学）；Ruhr University Bochum（波鸿鲁尔大学）；University of Amsterdam（阿姆斯特丹大学）；University of Notre Dame（圣母大学）；University of Duisburg-Essen（杜伊斯堡-埃森大学）
See, Hypothesize, Validate: Multimodal Agentic Framework for Discovering Governing PDEs
Indian Institute of Technology Delhi（印度德里理工学院）；Robert Bosch GmbH（罗伯特·博世有限公司）；TCS Research（塔塔咨询服务公司研究院）；Yardi School of Artificial Intelligence (ScAI)（亚迪人工智能学院）；Department of Applied Mechanics（应用力学系）
Directional Concentration Uncertainty: A representational approach to uncertainty quantification for generative models
Department of Mechanical Engineering, Iowa State University, Ames, IA, USA（机械工程系，爱荷华州立大学）；Pacific Northwest National Laboratory, Richland, WA, USA（太平洋西北国家实验室）；Department of Statistics, North Carolina State University, Raleigh, NC, USA（统计系，北卡罗来纳州立大学）
Agentic Physical AI toward a Domain-Specific Foundation Model for Energy Systems: A Case Study on Nuclear Reactor Control
The Grainger College of Engineering, Nuclear, Plasma & Radiological Engineering, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校格雷格学院工程学院、核等工程学院）；Department of Nuclear Engineering, Hanyang University（汉阳大学核工程系）；University of Texas - El Paso（德克萨斯大学埃尔帕索分校）；National Center for Supercomputing Applications（国家超级计算应用中心）；Department of Applied Mechanics, Indian Institute of Technology Delhi（印度德里理工学院应用力学系）；Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi（印度德里理工学院亚里人工智能学院）
Agentic DDQN-Based Scheduling for Licensed and Unlicensed Band Allocation in Sidelink Networks
Department of Electronics and Electrical Engineering（电子与电气工程系）；Institute of Communications Engineering（通讯工程研究所）；National Yang Ming Chiao Tung University（阳明交通大学）；Research Center for Information Technology Innovation（信息技术创新研究中心）；Academia Sinica（台湾“中央研究院”）；Bradley Department of Electrical and Computer Engineering（电气与计算机工程系）
LUCAID: Agentic Multimodal AI for Lung Cancer Precision Pathology
Institute of Pathology, Charité – Universitätsmedizin Berlin（柏林夏里特医学院病理学研究所）；Freie Universität Berlin（柏林自由大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Berlin Institute of Health at Charité – Universitätsmedizin Berlin（柏林夏里特医学院柏林卫生研究所）；Aignostics GmbH（Aignostics有限公司）；Machine Learning Group, Technical University of Berlin（柏林工业大学机器学习组）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（BIFOLD——柏林学习与数据基础研究所）；MVZ HPH Institut für Pathologie und Hämatopathologie GmbH（HPH病理及血液病理诊断中心有限公司）
Toward Autonomous O-RAN: A Multi-Scale Agentic AI Framework for Real-Time Network Control and Management
IDLab, Department of Information Technology at Ghent University - imec（IDLab，格鲁特大学信息科技系 - imec）；Department of Electrical and Computer Engineering, Princeton University（电气与计算机工程系，普林斯顿大学）
Seeing and Reasoning with Confidence: Supercharging Multimodal LLMs with an Uncertainty-Aware Agentic Framework
University College London（伦敦大学学院）；National Health Service（英国国家医疗服务体系）；University of Surrey（萨里大学）；Samsung AI Centre（三星人工智能中心）；Queen Mary University of London（伦敦玛丽女王大学）
Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities
University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Carnegie Mellon University（卡内基梅隆大学）；Nanyang Technological University（南洋理工大学）；University of Pennsylvania（宾夕法尼亚大学）；University of Southern California（南加州大学）；University of California, Berkeley（加州大学伯克利分校）
A Comprehensive Framework for Uncertainty Quantification of Voxel-wise Supervised Models in IVIM MRI
Department of Acute Brain（急性脑和心血管损伤部门）；Department of Neuroscience（神经科学部门）；Istituto di Ricerche Farmacologiche Mario Negri IRCCS（马里奥·内格里药理研究所）；Milan（米兰）；Italy（意大利）
Bayesian 3D Steerable CNNs: Enabling Equivariance and Uncertainty Quantification Simultaneously
Michigan Technological University（密歇根理工大学）；Johns Hopkins University（约翰霍普金斯大学）；The Center for Artificial Intelligence at the Institute of Computing and Cybersystems, Michigan Technological University（密歇根理工大学计算与网络系统研究所人工智能中心）
Uncertainty Quantification From Scaling Laws in Deep Neural Networks
Department of Physics, University of Illinois Urbana-Champaign, Urbana, IL, USA（伊利诺伊大学厄巴纳-香槟分校物理系）；Department of Physics, University of Toronto, Toronto, ON, Canada（多伦多大学物理系）；Vector Institute, Toronto, ON, Canada（向量研究所）
